Generating a data stream with configurable compression

ABSTRACT

One example method includes receiving a mixed data stream that was created using a first data stream and a second data stream, the mixed data stream having a compressibility of N, where N is a compressibility merging parameter, and the mixed data stream has a compressibility that is between a compressibility of the first data stream and a compressibility of the second data stream, providing the mixed data stream to an application and/or hardware, observing and recording a response of the application and/or hardware to the mixed data stream, and analyzing the response of the response of the application and/or hardware to the mixed data stream.

RELATED APPLICATIONS

This application is related to: U.S. Pat. No. 10,038,733 (Ser. No.14/489,317, filed Sep. 17, 2014), entitled GENERATING A LARGE,NON-COMPRESSIBLE DATA STREAM, issued Jul. 31, 2018; U.S. Pat. No.10,114,832 (Ser. No. 14/489,363, filed Sep. 17, 2014), entitledGENERATING A DATA STREAM WITH A PREDICTABLE CHANGE RATE, issued Oct. 30,2018; and, U.S. Pat. No. 10,114,850 (Ser. No. 14/489,295, filed Sep. 17,2014), entitled DATA STREAM GENERATION USING PRIME NUMBERS, issued Oct.30, 2018. This application is also related to: United States patentapplication (Ser. UNKNOWN—atty. docket 16192.256), entitled GENERATING ADATA STREAM WITH CONFIGURABLE CHANGE RATE AND CLUSTERING CAPABILITY,filed the same day herewith; United States patent application (Ser.UNKNOWN—atty. docket 16192.265), entitled GENERATING A DATA STREAM WITHCONFIGURABLE COMMONALITY, filed the same day herewith; United Statespatent application (Ser. UNKNOWN—atty. docket 16192.257), entitledGENERATING AND MORPHING A COLLECTION OF FILES IN A FOLDER/SUB-FOLDERSTRUCTURE THAT COLLECTIVELY HAS DESIRED DEDUPABILITY, COMPRESSION,CLUSTERING AND COMMONALITY, filed the same day herewith; U.S. Pat. No.10,163,371, (Ser. No. 15/420,633, filed Jan. 31, 2017), entitledROTATING BIT VALUES BASED ON A DATA STRUCTURE WHILE GENERATING A LARGE,NON-COMPRESSIBLE DATA STREAM, issued Dec. 25, 2018; and, U.S. Pat. No.10,235,134 (Ser. No. 15/420,614, filed Jan. 31, 2017), entitled ROTATINGBIT VALUES WHILE GENERATING A LARGE, NON-COMPRESSIBLE DATA STREAM,issued Mar. 19, 2019. All of the aforementioned patents and applicationsare incorporated herein in their respective entireties by thisreference.

FIELD OF THE INVENTION

Embodiments of the present invention generally relate to generation ofdata streams having various attributes. More particularly, at least someembodiments of the invention relate to systems, hardware, software,computer-readable media, and methods for generating data streams whosecompression is configurable.

BACKGROUND

Developers and other personnel often have a need to simulatecharacteristics of real world data streams that are generated byapplications that are in a developmental stage. Simulation of real worlddata stream characteristics, such as compressibility for example,enables the developer to identify and correct any problems, and enhanceperformance of the application, before the application, or a revision ofthe application, is rolled out.

Various algorithms have been developed for generation of data streams.However data streams generated by these algorithms may be relativelynarrow in terms of their applicability and usefulness. This may be dueto various factors. For example, the speed with which such streams aregenerated may not be adequate. As another example, data streamsgenerated by such algorithms may be incompressible. Further, such datastreams may not be deduplicatable. These, and other, factors may tend tolimit the effectiveness, in some applications, of the data streamsproduced by some data stream generation algorithms.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which at least some of the advantagesand features of the invention can be obtained, a more particulardescription of embodiments of the invention will be rendered byreference to specific embodiments thereof which are illustrated in theappended drawings. Understanding that these drawings depict only typicalembodiments of the invention and are not therefore to be considered tobe limiting of its scope, embodiments of the invention will be describedand explained with additional specificity and detail through the use ofthe accompanying drawings.

FIG. 1 discloses aspects of an example operating environment for someembodiments of the invention.

FIG. 2 discloses aspects of an example host configuration.

FIG. 3 discloses some general aspects of a configuration in which one ormore incompressible data streams are mixed with one or more compressibledata streams to generate a mixed data stream of a particularcompressibility.

FIGS. 3a-3g disclose an example portion of an incompressible datastream.

FIGS. 4a-4d disclose examples of ways in which multiple data streams canbe combined to create an output data stream having a particularcompressibility.

FIG. 5 is a flow diagram that discloses some general aspects of a methodfor generating mixed data streams.

DETAILED DESCRIPTION OF SOME EXAMPLE EMBODIMENTS

Embodiments of the present invention generally relate to generation ofdata streams having various attributes. More particularly, at least someembodiments of the invention relate to systems, hardware, software,computer-readable media, and methods for generating data streams whosecompression is configurable. The data in a given data stream may bereferred to herein as a dataset.

More particularly, example embodiments of the invention employ datastream mixing to generate a data stream having particular compressionproperties. Depending upon the implementation, two, or more, datastreams may be mixed. The resulting data stream created by the mixing oftwo or more data streams may be used in a variety of applications. Toillustrate, such a resulting, or synthesized, data stream may be used inapplications where high speed generation of a data stream, havingparticular compression properties, is needed for automated and/or manualtesting of an application, hardware, and/or other elements. Example datastreams may be generated at rates exceeding 1 GBPS. In at least someembodiments, compressible streams, and incompressible data streams, canbe generated by the methods and systems disclosed in one or more of theRelated Applications.

One of the data streams that is to be mixed with one or more other datastreams may have a compressibility of about 0%, although that is notrequired. Examples of such data streams, and methods for generatingthem, are disclosed in one or more of the Related Applications notedherein. Additionally, or alternatively, one of the data streams that isto be mixed with one or more other data streams may have acompressibility of about 100%, although that is not required.

In some embodiments, multiple data streams are mixed together togenerate a new data stream having a particular compressibility. Whereany two or more data streams are mixed together, the respective data ofthe data streams may be interleaved, such as on a data block, datasequence, or other, basis, to form the new data stream. Finally, thedata streams can be mixed in a variety of ways, such as clustered,uniform, random, or normalized mixing.

Advantageously then, embodiments of the invention may provide variousbenefits and improvements relative to the configuration and operation ofconventional hardware, software, systems and methods. For example, anembodiment of the invention enables customization of a data stream tomeet testing, analytical, and diagnostic needs in a computingenvironment. As well, an embodiment of the invention enables generationof a data stream having a particular, ‘non-zero,’ compressibility.Further, an embodiment of the invention enables generation of datastream having a particular commonality with respect to respective datastreams generated by computing entities in a population of computingentities. The compressibility feature helps to simulate data that ispartly compressible, which is a common data type in many applications.The commonality feature helps to simulate data that is common acrossmultiple groups of owners. This is useful in the context ofdeduplication engines and processes, which need to be both effective andefficient in their deduplication operations. Moreover, the flexibilityof embodiments of the invention enable generation of data streamsspecifically suited for performance of customized testing, analytical,and diagnostic, processes in a computing environment. Among otherthings, such embodiments enable the identification of areas whereimprovements may be made in the operation of an application and/orcomputing system hardware and other software.

It should be noted that the foregoing advantageous aspects of variousembodiments are presented only by way of example, and various otheradvantageous aspects of example embodiments of the invention will beapparent from this disclosure. It is further noted that it is notnecessary that any embodiment implement or enable any of suchadvantageous aspects disclosed herein.

A. Aspects of an Example Operating Environment

The following is a discussion of aspects of example operatingenvironments for various embodiments of the invention. This discussionis not intended to limit the scope of the invention, or theapplicability of the embodiments, in any way.

In general, embodiments of the invention may be implemented inconnection with systems, software, and components, that individuallyand/or collectively implement, and/or cause the implementation of, datageneration and data management operations. Such data managementoperations may include, but are not limited to, data read/write/deleteoperations, data deduplication operations, data backup operations, datarestore operations, data cloning operations, data archiving operations,and disaster recovery operations. Thus, while the discussion herein may,in some aspects, be directed to a discussion of data protectionenvironments and operations, the scope of the invention is not solimited. More generally then, the scope of the invention embraces anyoperating environment in which the disclosed concepts may be useful. Insome instances, embodiments of the invention generate data streams foruse in testing systems and applications in various environments, oneexample of which is a data protection environment.

A data protection environment, for example, may take the form of apublic or private cloud storage environment, an on-premises storageenvironment, and hybrid storage environments that include public andprivate elements, although the scope of the invention extends to anyother type of data protection environment as well. Any of these examplestorage environments, may be partly, or completely, virtualized. Thestorage environment may comprise, or consist of, a datacenter which isoperable to service read and write operations initiated by one or moreclients.

In addition to the storage environment, the operating environment mayalso include one or more host devices, such as clients for example, thateach host one or more applications. As such, a particular client mayemploy, or otherwise be associated with, one or more instances of eachof one or more applications that generate data that is desired to beprotected. In general, the applications employed by the clients are notlimited to any particular functionality or type of functionality. Someexample applications and data include email applications such as MSExchange, filesystems, as well as databases such as Oracle databases,and SQL Server databases. The applications on the clients may generatenew and/or modified data that is desired to be protected.

Any of the devices, including the clients, servers and hosts, in theoperating environment can take the form of software, physical machines,or virtual machines (VM), or any combination of these, though noparticular device implementation or configuration is required for anyembodiment. Similarly, data protection system components such asdatabases, storage servers, storage volumes (LUNs), storage disks,replication services, backup servers, restore servers, backup clients,and restore clients, for example, can likewise take the form ofsoftware, physical machines or virtual machines (VM), though noparticular component implementation is required for any embodiment.Where VMs are employed, a hypervisor or other virtual machine monitor(VMM) can be employed to create and control the VMs.

As used herein, the term ‘data’ is intended to be broad in scope. Thus,that term embraces, by way of example and not limitation, data segmentssuch as may be produced by data stream segmentation processes, datachunks, data blocks, atomic data, emails, objects of any type, files,contacts, directories, sub-directories, volumes, and any group of one ormore of the foregoing.

Example embodiments of the invention are applicable to any systemcapable of storing and handling various types of objects, in analog,digital, or other form. Although terms such as document, file, block, orobject may be used by way of example, the principles of the disclosureare not limited to any particular form of representing and storing dataor other information. Rather, such principles are equally applicable toany object capable of representing information.

With particular attention now to FIG. 1, one example of an operatingenvironment is denoted generally at 100. In some embodiments, theoperating environment may comprise, or consist of, a data protectionenvironment. The operating environment can include an enterprisedatacenter, or a cloud datacenter, or both. The data protectionenvironment may support various data protection processes, includingdata replication, data deduplication, cloning, data backup, and datarestoration, for example. As used herein, the term backups is intendedto be construed broadly and includes, but is not limited to, partialbackups, incremental backups, full backups, clones, snapshots,continuous replication, and any other type of copies of data, and anycombination of the foregoing. Any of the foregoing may, or may not, bededuplicated.

In the illustrated example, the operating environment 100 may includeany type and number of data generators 102, 104 and 106. In general, thedata generators 102 . . . 106 may be any software, hardware, orcombination of software and hardware, that is operable to generate data.The software may, in some embodiments, comprise, or consist of, one ormore applications, and the applications may be of any type. Thus, insome cases, one or more of the data generators 102 . . . 106 maycomprise a client device that hosts one or more applications. The datagenerated by a data generator may, or may not, be targeted forprotection and backed up, such as at a cloud datacenter for example. Insome embodiments, one, some, or all, of the data generators 102 . . .106 may comprise a purpose-built entity, which may comprise hardwareand/or software, specifically configured to generate incompressible datastreams and/or compressible data streams.

As further indicated in FIG. 1, the operating environment 100 mayinclude a mixer 108. In general, the mixer 108 is operable to combinedata streams from the data generators 102 . . . 106 so as to create anew data stream. Each new data stream created by the mixer 108 can begenerated in such a way as to have particular compression attributes.

The operation of the mixer 108 may be configurable via variousparameters, and these parameters may help to shape the properties of theoutput data stream. These parameters of an output data stream created bythe mixer 108 may be specified, for example, by a user using a userinterface (UI) 108 a and/or application program interface (API)associated with the mixer 108. The UI may be any type of user interfaceincluding, but not limited to, a graphical user interface (GUI), or acommand line interface (CLI). The mixer 108 can then use the user inputto generate a new data stream by mixing two or more input data streams.User inputs provided by way of the UI, and/or other mechanism, mayinclude, but are not limited to, any one or more of: the amount of dataof the output stream; one or more self seeds; one or more base seeds;the identity of the source data streams; the identity of the datagenerators; a desired commonality factor (CF); a respectivecompressibility parameter for each source data stream; and, a desiredcompressibility parameter for the data stream generated by the mixer108. In an embodiment, the mixer 108 may combine multiple data streamsfrom each of a plurality of respective sources, such as from the datagenerators 102 . . . 106 for example.

The mixer 108 may be implemented as hardware, software, or a combinationof hardware and software. In some embodiments, the mixer 108 takes theform of an application that may be hosted on a server, or any other typeof host device. The mixer 108 may reside at a user premises, at a clouddatacenter, and/or at any other site. In some embodiments, the mixer 108may be an element of another system or device, such as a deduplicationserver for example. Thus, in such embodiments, an output data streamgenerated by the mixer 108 may then be deduplicated by the deduplicationserver. However, the mixer 108 need not be an element of a deduplicationserver and, in other embodiments, the output data stream generated bythe mixer 108 may be provided to a deduplication server fordeduplication.

With continued reference to FIG. 1, the mixer 108 may constitute anelement of, or communicate with, a test environment 109. The testenvironment 109 may include, for example, one or more applications 110and/or one or more hardware devices 112. In general, the data streamsgenerated by the mixer 108 may be provided by the mixer 108 to anapplication 110 and/or hardware device 112 for testing, analysis, and/ordiagnostic, operations. Such data streams may, or may not, bededuplicated before being provided to the test environment 109.

More particularly, the data streams generated by the mixer 108 may beprovided to, and utilized by, an application 110 and/or hardware device112. The outputs and/or other responses of the application 110 and/orhardware 112 can then be provided to an evaluation module 114 foranalysis and diagnostics. In some embodiments, the evaluation module 114is an element of the mixer 108. In other embodiments however, theevaluation module 114 is separate and distinct from the mixer 108.

By generating data streams using inputs from one or more datagenerators, the mixer 108 enables the testing of application 110 and/orhardware 112 so that analyses may be performed, and solutions identifiedfor any problems observed. The flexibility of embodiments with respectto customizing the commonality and/or compressibility of data streamsgenerated by the mixer 108 enables a wide variety of test and evaluationscenarios to mimic, or replicate, real world conditions.

B. Example Host and Server Configurations

With reference briefly now to FIG. 2, any one or more of the datagenerators 102 . . . 106, mixer 108, test platform 109, applications110, hardware 112, evaluation module 114, entity 306, and mixer 308, cantake the form of, or include, or be implemented on, or hosted by, aphysical computing device, one example of which is denoted at 200. Aswell, where any of the aforementioned elements comprise or consist of avirtual machine (VM), that VM may constitute a virtualization of anycombination of the physical components disclosed in FIG. 2.

In the example of FIG. 2, the physical computing device 200 includes amemory 202 which can include one, some, or all, of random access memory(RAM), non-volatile random access memory (NVRAM) 204, read-only memory(ROM), and persistent memory, one or more hardware processors 206,non-transitory storage media 208, UI device 210, and data storage 212.One or more of the memory components 202 of the physical computingdevice 200 can take the form of solid state device (SSD) storage. Aswell, one or more applications 214 are provided that comprise executableinstructions. Such executable instructions can take various formsincluding, for example, instructions executable to perform any method orportion thereof disclosed herein, and/or executable by/at any of astorage site, whether on-premises at an enterprise, or a cloud storagesite, client, datacenter, backup server, blockchain network, orblockchain network node, to perform functions disclosed herein. As well,such instructions may be executable to perform any of the otheroperations disclosed herein including, but not limited to, data streammixing, data stream evaluation and analysis, data stream generation,read, write, backup, and restore, operations and/or any other dataprotection operation, auditing operations, cloud service operations.

C. Modified Data Stream with Configurable Compression

Directing attention now to FIG. 3, details are provided concerningsystems and processes for generating data streams having auser-configurable compression. In the example 300 of FIG. 3, one or moreincompressible data streams 302 are mixed together, or merged, with oneor more compressible data streams 304. The data streams may be mixedtogether in a uniform, random, normalized, or clustered distribution,manner. The extent, if any, to which any particular input and/or outputdata stream, or data streams, is/are compressible, can be specified by auser or a computing entity. As well, the way, or ways, in which the twoor more data streams are mixed together, can be specified by a user or acomputing entity. It should be noted that the arrangement 300 disclosedin FIG. 3 is presented only by way of example, and it will be apparentto one having the benefit of this disclosure that the principlesdisclosed in relation to FIG. 3 are extendible to a variety of othercircumstances and configurations as well.

In the particular example of FIG. 3, an incompressible data stream 302may be generated, such as by a data generator, examples of which aredisclosed herein. In some embodiments, the incompressible data stream302 may be generated by an entity 306 specifically configured togenerate incompressible data streams. As indicated, the incompressibledata stream 302 may be generated based on an initialization parameterthat may be referred to as a ‘seed’ or ‘seed value.’ Other example datagenerators are disclosed in the Related Applications.

The incompressible data stream 302 may be referred to as having acompressibility that is 0%, or about 0%. Thus, the incompressible datastream 302 may comprise, or consist of, a sequence of blocks that areeach unique. To illustrate, the incompressible data stream 302 mayinclude the sequence of blocks ‘ABCDEF . . . ’ As this exampleillustrates, there is, in the sequence, only a single instance of eachblock. Thus, the sequence ABCDEF of the data stream 302 cannot becompressed since there are no duplicate blocks that can be removed fromthe sequence to reduce, that is, compress, the size of the sequence.Examples of incompressible data streams, and processes for generatingincompressible data streams, are disclosed in one or more of the RelatedApplications.

On the other hand, the data stream 304 may be partly, or fully,compressible. In the latter case, the data stream 304 may be referred toas having a compressibility that is 100%, or about 100%. Thus, thecompressible data stream 304 may comprise, or consist of, a sequence ofcharacters, parts, or other pieces of data, that are all the same. Toillustrate, the compressible data stream 304 may include the sequence‘XXXXXX . . . ’ As this example illustrates, there is, in this sequence,multiple instances of the same character. Thus, the sequence XXXXXX ofthe data stream 304 is highly compressible, though maybe not 100%compressible, since all the characters in the sequence are duplicates,and nearly all of the duplicate characters can be removed from thesequence to reduce, that is, compress, the size of the sequence. Furtherexamples of data stream compression are discussed below at FIGS. 4a -4d.

In some embodiments, the data streams 302 and 304 may be produced by thesame entity, such as an application hosted by a host device. In otherembodiments, the data stream 302 may be generated by a purpose-builtentity, which may comprise hardware and/or software, specificallyconfigured to generate incompressible data streams, and the data stream304 may be produced by a data generator such as is disclosed herein.

With continued reference to FIG. 3, the data streams 302 and 304 may beprovided to a mixer 308 which may be similar, or identical, to the mixer108 disclosed in FIG. 1. As such, the inputs (seed, N) to the mixer 308are the seed that was used as a basis for generation of theincompressible data stream 302, and ‘N,’ or the percentage of data fromthe compressible data stream 304 that will be used in the generation ofthe output stream by the mixer 308. To illustrate with an example, acompressible data stream 310 generated by the mixer 308 may comprise 70%incompressible data from the data stream 302, and 30%, that is, ‘N’ %,data from the data stream 304. The combination, by the mixer 308, ofthese two data streams results in an output data stream 310 that is 30%compressible. That is, the output data stream 310 may be compressed to70% of its initial size. As the foregoing example illustrates, the valueof ‘N’ can be selected as necessary.

With continued reference to FIG. 3, a further example is illustrative.If ‘N’ is specified to be 33%, such as by a user using a UI incommunication with the mixer 308, the data stream 310 generated by themixer 308 is about 33% compressible. Thus, part of the data in anexample output data stream 310 may be supplied by the data stream 302,and the other part of the data in an example output data stream 310 maybe supplied by the data stream 304. More generally, any number ‘X’ ofinput data streams can be mixed together to generate an output datastream, where X is a whole integer 2. An example sequence of the outputdata stream 310 may include 9 blocks and look like ‘ABXCDXEFX . . . ’,where 6/9 of the blocks (that is, AB, CD, and EF), or about 67% of thedata, are taken from the data stream 302, and 3/9 of the blocks (that isX, X, X), or about 33% of the data, are taken from the data stream 304.

D. Configurable Compressibility

The discussion thus far has addressed various concepts concerning thecombination of two or more data streams, each having a respectivecompressibility in a range of about 0% to about 100%, to generate anoutput data stream with a desired compressibility. In general, a datastream of any degree of compressibility (in a range of about 0% to about100%) can be generated in connection with embodiments of the invention.That data stream can be generated by mixing two or more data streams inany of a wide variety of different ways which are disclosed hereinand/or which would be apparent from this disclosure. As such, thefollowing illustrations of ways in which data streams of desiredcompressibility can be created are provided only by way of example, andnot limitation.

With attention briefly to FIGS. 3a-3g , an example block 300 of data(approximately 8 KB in size) of an incompressible data sequence, isdisclosed. The block 300, which can be generated by a data generator,begins on FIG. 3a and ends at the bottom of FIG. 3g . The portions ofthe block 300 denoted at 302 and 304 are each 128 byte examples that areused for illustrative purposes in the following discussion. In each ofthe examples of FIGS. 4a-4d , a mixed data stream can includecompressible data such as can be provided by way of a data stream suchas data stream 304 (FIG. 3), and the mixed data stream can includeincompressible data such as can be provided by way of a data stream suchas data stream 302 (FIG. 3).

With general reference now to FIGS. 4a-4d , details are providedconcerning some specific examples of how multiple data streams can becombined to create an output data stream having a particularcompressibility. The examples disclosed in FIGS. 4a-4d can beimplemented, for example, by a mixer, embodiments of which are disclosedherein.

Turning next to FIG. 4a , it will be assumed for the purposes ofdiscussion that the example block portions 302 and 304 (see FIG. 3a ),which together comprise 256 bytes, represent respective 8 KB blocks 402and 404. The example disclosed in FIG. 4b illustrates one way in which adesired level of compressibility can be achieved with respect to theexample blocks 402 and 404. As is evident from the blocks 402 and 404,those blocks are incompressible because any sequence of bytes (where asequence length is 1 or more) does not significantly occur again inthose blocks. Thus, if the 256 byte data sequence shown in FIG. 4a weresent to a dedupe engine, the blocks 402 and 404 could not be compressedby the dedupe engine.

In the example of FIG. 4b , however, blocks 402 a and 404 a representblocks 402 and 404 after a desired compressibility has been introduced.It is assumed for the purposes of illustration that the blocks 402 a and404 a, when created, should have a compressibility of about 75%, thatimplementation of the desired compressibility takes place at the blocklevel, and that the blocks are 8 KB in size. It is noted however, a userand/or computing entity, such as a dedupe engine, can specify one ormore parameters such as, but not limited to: (i) the block size; (ii)the desired compressibility; and, (iii) the level, block or otherwise,at which compression should be performed. Additional, or alternative,parameters may be considered when compressibility is to be implementedin a data stream.

With continued reference to FIG. 4b , no change is made to the first 32bytes of the block, and so those bytes remain at the same originaloffset. However, the remaining 96 bytes of block 402 are replaced with00, where a 00 refers to a byte that has a value of 0 or in hexadecimals0x00. The 00 values are located at bytes 33-128, which results in ablock 402 b having a compressibility of about 75%. Similarly with regardto block 404 a, and in view of the insertion of 00 values at bytes33-128, the original values at bytes 33-64 are now re-positioned at adifferent offset, that is, at bytes 129-160, followed by 00 values atthe next 96 bytes. Thus, blocks 402 a and 404 a now each have acompressibility of about a 75%. It will be appreciated that theaforementioned process can be performed repeatedly until an entire datastream, or portion thereof, comprises, or consists of, blocks that areeach compressible to the specified extent, 75% in this example.

Turning now to FIG. 4c , a variation of the processes concerning FIG. 4bis disclosed. In general, FIG. 4c discloses the notion that to achieve adesired compressibility at the block, or other level, the compressibleportion of the block can be made up of any compressible data. Thus, asindicated in FIG. 4c , the compressible data need not be 00 values. Inthe example blocks 402 b and 404 b, the compressible data is 41424344(ABCD) and 45464748 (EFGH), respectively. It can be seen that this datawill compress as readily as if 00 values had been used, as in theexample of FIG. 4b . It can also be seen that the compressible data usedcan, but is not required to, be different for different blocks of thesame data stream. Thus, the compressible data for block 402 b is41424344, while the compressible data for block 404 b is 45464748.

As well, and similar to the example of FIG. 4b , the offset of the first32 bytes of block 402 b is unchanged by the insertion of thecompressible data 41424344. However, the original bytes 33-64 are stillpresent, but now positioned at an offset of 129-160 as a result of theinsertion of the compressible data 41424344.

In the examples of FIGS. 4b and 4c , compressible data, such as 00values, or other compressible data such as U.S. Pat. No. 41,424,344(ABCD) and 45464748 (EFGH), was inserted into the data blocks 402 a, 404a, 402 b, and 404 b, so as to achieve the desired block levelcompressibility. In other embodiments, processes other than datainsertion can be used to achieve a desired compressibility whengenerating a mixed data stream. Accordingly, attention is directed nowto FIG. 4d , where blocks 402 c and 404 c are disclosed. In thisexample, compressible data is written over some of the incompressibledata, rather than being inserted in the incompressible data, as in theexamples of FIGS. 4b and 4 c.

Thus, as indicated in the example of FIG. 4d , and with reference firstto block 402 c, compressible data is written over all bytes after thefirst 32 bytes. Similar to other examples disclosed herein, there is nochange to the offset of the first 32 bytes. The effect of the overwritecan be seen more clearly with reference to block 404 c. In block 404 c,compressible data is written over some of the incompressible data,specifically, bytes 33-64. Thus, rather than bytes 33-128 being moved toan offset of 129-160, as occurred when compressible data was inserted(FIGS. 4b and 4c ), those bytes 33-128 are overwritten in the example ofFIG. 4d , and the data originally at bytes 129-160 thus remains at the129-160 offset. Bytes 161-256 of block 404 c are overwritten withcompressible data.

E. Example Data Stream Mixing Methodologies

With the foregoing examples in view, it was noted herein that that whentwo or more data streams are mixed together, the mixing of the data inthe two data streams can be performed in various ways. For example, themixing may be uniform, clustered, random, normalized, or any othermathematical distribution or mix of one or more of these exampledistributions. The particular mixing process employed can be selectedbased on the particular circumstances involved. Some examples of thesemixing processes are discussed below.

An understanding of some aspects of example mixing processes can beappreciated with reference to an example. Particularly, it is useful inat least some circumstances to keep compression applied to each blockthat a deduplication (or ‘dedupe’) engine may create. For a dedupeengine that typically creates 8 KB blocks, for example, parts areselected from the 0% and 100% compressibility streams such that theirsum is about 8K. So, for a 75% desired compression, 2 KB of data comesfrom the 0% compressible stream and 6 KB comes from the 100%compressible stream. However, the same result would not be achieved if250 KB were picked from 0% compressible stream followed by 750 KB fromthe 100% compressible stream. Thus, the particular way in which data ismixed has implications with respect to the compressibility ultimatelyachieved in a mixed data stream which includes that data.

For example, a dedupe engine may need a particular average, or overall,compressibility in a data stream, but in this particular case, thecompressibility should not always be the same in the data stream. Thus,the compression logic in this example would be configured in such a waythat the average compressibility over a larger number of blocks is thedesired value, although the compressibility of any given block, or evena group of blocks, may not be the average compressibility.

To illustrate with an example, a mixed data stream might be configuredso that 20% of the blocks have a compressibility of 65%, 20% of theblocks have a compressibility of 70%, 20% of the blocks have acompressibility of 75%, 20% of the blocks have a compressibility of 80%,and 20% of the blocks have a compressibility of 90%. This would producea mixed stream having an average compressibility of 75%, even thoughonly 20% of the blocks have 75% compressibility. This variation, in themixed data stream, of compressibility may more accurately reflect somereal world data streams than would a mixed data stream having blocksthat are all 75% compressible. As discussed below, uniform, random,normalized, and clustered, mixing of blocks may determine where, in asequence of blocks, blocks of different compressibility are created.

For example, one technique for mixing data streams is to uniformly mix,or merge, the data of the constituent streams. For example, if a 100Gstream is to have 90% unique data, and 10% common data, a nonuniformmixing of the data is to arrange the data in serial fashion where, forexample, the 10% common data is followed by the 90% unique data, or viceversa. In contrast, a uniform mixing of the data might take the form,for example, of data arranged in the mixed data stream thus: 1 MB(common), 9 MB (unique), 1 MB (common), 9 MB (unique) . . . until amixed data stream of 100 GB is defined. In this way, the common data andthe unique data are uniformly distributed in the mixed data stream. Insome cases, it may be useful to set a minimum size for the chunks orgroupings of data. In the example above, the chunks are either 1 MB or 9MB. If the chunk size is too small, a deduplication server may not beable to discern commonalties in the data and, as a result, all of thedata in the data stream may, incorrectly, appear to be unique to thededuplication server.

In the example of FIG. 3, the output data stream from the mixer 308reflects the application of a uniform mixing process to the input datastreams. Particularly, the output data stream is of the form: 2 parts(AB—from incompressible stream), followed by 1 part (X—from compressiblestream), followed by 2 blocks (CD—from incompressible stream), followedby 1 part (X—from compressible stream), followed by 2 parts (EF fromincompressible stream), followed by 1 block (X—from compressible stream). . . and so forth.

Another method of mixing data streams is to mix the data randomly. Inthis approach to mixing data streams, the chunk sizes are random. Incontrast, in the preceding example, the chunk sizes are not random butare either 1 MB or 9 MB. For example, chunk sizes may be selected as100K, 75K, 125K . . . . In this case, the stream size of the mixedstream may be specified, such as 100 GB for example. As well, a minimumand/or maximum chunk size may be specified, and random chunk sizeswithin those bounds may be specified. With reference to the foregoingexample, a minimum chunk size of 50K may be specified and/or a maximumchunk size of 150K may be specified. As noted, the minimum chunk sizemay help to ensure that the granularity of the mixed stream is not sofine that a deduplication server would fail to recognize common data inthe mixed stream.

Still another approach to mixing, or merging, multiple data streamsinvolves a normalized mixing of the data from the constituent datastreams. In a data stream exhibiting normalized mixing, the data chunksmay be arranged thus: unique data; mixed data; unique data. Thus, in adata stream with normalized mixing, the mixed data is distributed in aparticular portion, or portions, of the data stream.

Yet another approach to mixing or merging multiple data streams involvesa clustered mixing of the data in the data stream. In particular, themixed data stream may be configured such that the data stream includesportions where data of the constituent streams is not mixed together,and the data stream includes other portions where data of theconstituent data streams is mixed together.

It is noted that multiple different mixing techniques may be employed inconnection with a particular mixed data stream. Thus, the techniquesnoted above are presented by way of example only, and still othertechniques can be defined and implemented that employ two or more mixingprocesses to create a mixed data stream.

F. Aspects of Some Example Methods

With reference now to FIG. 5, details are provided concerning aspects ofexample methods for mixing two or more data streams, where one exampleof such a method is denoted generally at 500. The method 500 may beperformed by and/or at the direction of a mixer, examples of which aredisclosed herein. Some parts of the method 500 may be performed by otherentities, such as a test platform for example. In general however, thefunctional allocation indicated in FIG. 5 is provided only by way ofexample and, in other embodiments, the functions disclosed in FIG. 5 maybe allocated in various other ways.

The method 500 may begin at 502 when multiple data streams 2 . . . n,where n is ≥2, are received at a mixer. One or more of the data streamsmay be received from a data generator. As well, one or more of the datastreams may be received from an entity specifically configured togenerate data streams. In some cases, two or more of the data streamsare received from a common entity, while in other cases, two or more ofthe data streams are received from different respective entities. Eachof the received data streams may have respective compressibilitycharacteristics.

After, or before, receipt of the ‘n’ data streams 502, the mixer mayalso receive inputs in the form of one or more merging parameters 504that are usable by the mixer to create a mixed data stream havingparticular characteristics. Such characteristics include, for example,compressibility and commonality. The merging parameters 504 may bereceived from a user by way of a UI or API for example. In someembodiments, the mixer may affirmatively access a library, for example,and retrieve one or more of the merging parameters.

Using the merging parameters, the mixer is then able to merge 506 thereceived data streams to create a mixed data stream havingcharacteristics specified by the merging parameters. The data streamsmay be merged together 506 in any of a variety of ways. For example, themixer may employ a uniform, random, normalized, or clustered mixingprocess, or a combination of these, to generate 506 the output datastream.

The mixed data stream can then be output 508 by the mixer. The mixeddata stream possesses the compressibility characteristics specified bythe merge parameters. The mixer may output 508 the mixed data stream toany of a variety of recipients. In some cases, the mixed data stream maybe stored. Additionally, or alternatively, the mixed data stream may beoutput to 508, and received by 510 a test platform.

The test platform may use the data stream as a basis for performingtesting operations 512. The testing operations 512 may involve, forexample, providing the data stream to an application and/or hardware,and then observing and recording the response of the application and/orhardware to the data stream. In at least some embodiments, the datastream mimics, or duplicates, real world conditions. In this way,personnel, such as developers, are able to observe the response of anapplication, for example, to the data. The response of the applicationand/or hardware may be stored in some embodiments. As well, simulatedstreams according to embodiments of the invention may be used bycustomers to test the effectiveness of a dedupe solution that thecustomer is considering to purchase, since the customer may not want tosend their real data to the new platform under consideration either forsecurity concerns or for the concern related to breaking their normaloperating environment.

The data stream and/or the response information may then be analyzed514. Among other things, such analysis 514 may involve identifying anyproblems with the operation of the application and/or hardware to whichthe data stream was supplied during testing 512. The analysis 514 mayalso include identifying and implementing one or more corrective actionsto resolve the problems that were identified during testing 512.

In this way, embodiments of the invention enable testing of applicationsand other software, as well as hardware, for example, during adevelopment process so as to help ensure that the applications,software, and hardware, will operate as expected. This may reduce, oreliminate, one or more problems that would otherwise be experienced by apurchaser and/or user of the applications, hardware, and software.Further, because mixed data streams generated according to embodimentsof the invention are highly configurable in terms of theircompressibility and commonality, at least, such mixed data streams canbe generated to suit a variety of conditions and scenarios. Variousother advantages of example embodiments of the invention will beapparent from the present disclosure.

G. Example Computing Devices and Associated Media

The embodiments disclosed herein may include the use of a specialpurpose or general-purpose computer including various computer hardwareor software modules, as discussed in greater detail below. A computermay include a processor and computer storage media carrying instructionsthat, when executed by the processor and/or caused to be executed by theprocessor, perform any one or more of the methods disclosed herein.

As indicated above, embodiments within the scope of the presentinvention also include computer storage media, which are physical mediafor carrying or having computer-executable instructions or datastructures stored thereon. Such computer storage media can be anyavailable physical media that can be accessed by a general purpose orspecial purpose computer.

By way of example, and not limitation, such computer storage media cancomprise hardware storage such as solid state disk/device (SSD), RAM,ROM, EEPROM, CD-ROM, flash memory, phase-change memory (“PCM”), or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other hardware storage devices which can be used tostore program code in the form of computer-executable instructions ordata structures, which can be accessed and executed by a general-purposeor special-purpose computer system to implement the disclosedfunctionality of the invention. Combinations of the above should also beincluded within the scope of computer storage media. Such media are alsoexamples of non-transitory storage media, and non-transitory storagemedia also embraces cloud-based storage systems and structures, althoughthe scope of the invention is not limited to these examples ofnon-transitory storage media.

Computer-executable instructions comprise, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Although the subject matter has been described inlanguage specific to structural features and/or methodological acts, itis to be understood that the subject matter defined in the appendedclaims is not necessarily limited to the specific features or actsdescribed above. Rather, the specific features and acts disclosed hereinare disclosed as example forms of implementing the claims.

As used herein, the term ‘module’ or ‘component’ can refer to softwareobjects or routines that execute on the computing system. The differentcomponents, modules, engines, and services described herein may beimplemented as objects or processes that execute on the computingsystem, for example, as separate threads. While the system and methodsdescribed herein can be implemented in software, implementations inhardware or a combination of software and hardware are also possible andcontemplated. In the present disclosure, a ‘computing entity’ may be anycomputing system as previously defined herein, or any module orcombination of modules running on a computing system.

In at least some instances, a hardware processor is provided that isoperable to carry out executable instructions for performing a method orprocess, such as the methods and processes disclosed herein. Thehardware processor may or may not comprise an element of other hardware,such as the computing devices and systems disclosed herein.

In terms of computing environments, embodiments of the invention can beperformed in client-server environments, whether network or localenvironments, or in any other suitable environment. Suitable operatingenvironments for at least some embodiments of the invention includecloud computing environments where one or more of a client, server, orother machine may reside and operate in a cloud environment.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

1. A method, comprising: receiving a mixed data stream that wasgenerated using a first data stream and a second data stream, the mixeddata stream having a compressibility of N, where N is a compressibilitymerging parameter, and the mixed data stream has a compressibility thatis between a compressibility of the first data stream and acompressibility of the second data stream; providing the mixed datastream as an input to an application and/or to hardware; recording aresponse of the application and/or of the hardware to the mixed datastream input; and analyzing the response of the application and/or ofthe hardware to the mixed data stream input.
 2. (canceled)
 3. The methodas recited in claim 1, wherein the mixed data stream is provided to theapplication, and the application is a data deduplication application. 4.The method as recited in claim 1, wherein analyzing the response of theapplication and/or of the hardware comprises identifying any problemswith the operation of the application and/or of the hardware.
 5. Themethod as recited in claim 4, wherein analyzing the response of theapplication and/or of the hardware comprises identifying andimplementing a corrective action associated with an identified problem.6. The method as recited in claim 1, further comprising modifying theapplication and/or the hardware based on an outcome of the analyzing. 7.The method as recited in claim 1, further comprising modifying thecompressibility N of the mixed data stream.
 8. The method as recited inclaim 1, wherein the mixed data stream was generated based in part on aseed, and one of the first data stream and the second data stream isincompressible.
 9. The method as recited in claim 1, wherein the mixeddata stream has a particular commonality factor (CF) with respect to thefirst data stream and the second data stream.
 10. The method as recitedin claim 1, wherein the mixed data stream comprises an interleaving ofdata of the first data stream with data of the second data stream. 11.The method as recited in claim 1, wherein data in the mixed data streamembodies any one or more of the following mixes of data of the firstdata stream and data of the second data stream: a uniform mix; a randommix; a clustered mix; or, a normalized mix.
 12. A non-transitory storagemedium having stored therein computer-executable instructions which areexecutable by one or more hardware processors to perform operationscomprising: receiving a mixed data stream that was generated using afirst data stream and a second data stream, the mixed data stream havinga compressibility of N, where N is a compressibility merging parameter,and the mixed data stream has a compressibility that is between acompressibility of the first data stream and a compressibility of thesecond data stream; providing the mixed data stream as an input to anapplication and/or to hardware; recording a response of the applicationand/or of the hardware to the mixed data stream input; and analyzing theresponse of the application and/or of the hardware to the mixed datastream input.
 13. The non-transitory storage medium as recited in claim12, wherein the mixed data stream is provided to the application, andthe application is a data deduplication application.
 14. Thenon-transitory storage medium as recited in claim 12, wherein analyzingthe response of the application and/or of the hardware comprisesidentifying any problems with the operation of the application and/or ofthe hardware.
 15. The non-transitory storage medium as recited in claim14, wherein analyzing the response of the application and/or of thehardware comprises identifying and implementing a corrective actionassociated with an identified problem.
 16. The non-transitory storagemedium as recited in claim 1, wherein the operations further comprisemodifying the application and/or the hardware based on an outcome of theanalyzing.
 17. The non-transitory storage medium as recited in claim 12,wherein the operations further comprise modifying the compressibility Nof the mixed data stream.
 18. The non-transitory storage medium asrecited in claim 12, wherein the mixed data stream was generated basedin part on a seed, and one of the first data stream and the second datastream is incompressible.
 19. The non-transitory storage medium asrecited in claim 12, wherein the mixed data stream has a particularcommonality factor (CF) with respect to the first data stream and thesecond data stream.
 20. The non-transitory storage medium as recited inclaim 12, wherein the mixed data stream comprises an interleaving ofdata of the first data stream with data of the second data stream. 21.The non-transitory storage medium as recited in claim 12, wherein datain the mixed data stream embodies any one or more of the following mixesof data of the first data stream and data of the second data stream: auniform mix; a random mix; a clustered mix; or, a normalized mix.